alinasahoo/tsf-grip

This repository contains the solutions of all my tasks for the internship at "The Sparks Foundation" under the graduate rotational internship programme as "Data Science & Business Analytics" Intern.

The Sparks Foundation -Graduation Rotational Internship Program

This repository is dedicated to the completion of all my tasks from The Sparks Foundation (Graduate Rotational Internship Program). As of now, I will be updating the tasks from my domain : Data Science and Business Analytics for the November 2020 batch.

Tools/IDE : Python/Google Colab/Jupyter Notebook

Technical : Task 1 - Prediction using Supervised ML (Level - Beginner)

Predict the percentage of an student based on the no. of study hours.

This is supposed to be done with linear regression as we will be using just 2 variables. Data for this model can be found at : http://bit.ly/w-data.

What are we supposed to do with the given dataset?

We need to predict the score of the student if he/she studies for 9.25 hrs/day.

Technical : Task 2 - Prediction using Unsupervised ML (Level - Beginner)

Predict the optimum number of clusters, from the given "iris" dataset and represent it visually.

I will be implementing this with the help of K-Means Clustering algorithm. Dataset for this model can be found at : https://bit.ly/3kXTdox.

What are we supposed to do with the given dataset?

We need to predict the optimum number of clusters and it's visualization.

Technical : Task 3 - Exploratory Data Analysis - Retail (Level - Beginner)

Perform ‘Exploratory Data Analysis’ on dataset ‘SampleSuperstore’

I will be doing this with the help of python libraries i.e. matplotlib and seaborn. Dataset can be found at : https://bit.ly/3i4rbWl.

What are we supposed to do with the given dataset?

As a business manager, we will try to find out the weak areas where we can work tomake more profit. Also, what all business problems can be derived by exploring the data.

Technical : Task 4 - Exploratory Data Analysis - Terrorism (Level - Intermediate)

Perform ‘Exploratory Data Analysis’ on dataset ‘Global Terrorism’

I will be doing this with the help of seaborn, plotly and folium libraries in python. Dataset can be found at : https://bit.ly/2TK5Xn5

What are we supposed to do with the given dataset?

As a security/defense analyst, we will try to find out the hot zone of terrorism. Also, what all security issues and insights can be derived by EDA.